Systems and methods for multi-target tracking and autofocusing based on deep machine learning and laser radar

ABSTRACT

Systems and methods for recognizing, tracking, and focusing a moving target are disclosed. In accordance with the disclosed embodiments, the systems and methods may recognize the moving target travelling relative to an imaging device; track the moving target; and determine a distance to the moving target from the imaging device.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

TECHNICAL FIELD

The present disclosure relates generally to imaging technology and, moreparticularly, to methods and systems for multi-target tracking andautofocusing based on deep machine learning and laser radar.

BACKGROUND

Movable objects, such as unmanned aerial vehicles (“UAV”) (sometimesreferred to as “drones”), include pilotless aircraft of various sizesand configurations that can be remotely operated by a user and/orprogrammed for automated flight. UAVs may be equipped with imagingdevices to capture footage from stationary and/or moving perspectivesthat may be otherwise too challenging for personnel to capture.Additionally, UAVs may be equipped to collect imaging data over acertain period of time or for the duration of travel from one locationto another. In these circumstances, the UAV may be controlled inconjunction with information gathered using optical or sensory equipmentto recognize, follow (“track”), and focus on target objects (“targets”),such as people, vehicles, moving objects, stationary objects, etc. toachieve high-quality desirable images.

SUMMARY

The methods and systems for multi-target tracking and focusing based ondeep machine learning and laser radar in the embodiments disclosedherein overcome disadvantages of conventional systems.

In one aspect, the present disclosure relates to a method forrecognizing, tracking and focusing a moving target. The method includesrecognizing the moving target travelling relative to an imaging device;tracking the moving target; and determining a distance to the movingtarget from the imaging device.

In another aspect, the present disclosure relates to a system forrecognizing, tracking and focusing a moving target. The system includesa controller having one or more processors. The controller may beconfigured to: recognize the moving target travelling relative to animaging device; track the moving target; and determine a distance to themoving target from the imaging device.

In yet another aspect, the present disclosure relates to an unmannedaerial vehicle (UAV) system. The UAV system may include a controller incommunication with multiple devices. The controller may be configuredto: recognize the moving target travelling relative to an imagingdevice; track the moving target; and determine a distance to the movingtarget from the imaging device.

In yet another aspect, the present disclosure relates to anon-transitory computer-readable medium storing instructions that, whenexecuted, cause a computer to perform a method of recognizing, trackingand focusing a moving target. The method includes recognizing the movingtarget travelling relative to an imaging device; tracking the movingtarget; and determining a distance to the moving target from the imagingdevice.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of an exemplary movable object with acarrier and a payload and a control terminal consistent with embodimentsof the present disclosure;

FIGS. 2A and 2B are schematic diagrams of exemplary control terminalsthat may be used with embodiments consistent with the presentdisclosure;

FIG. 3 is a schematic diagram of an exemplary controller that may beused with embodiments of the present disclosure;

FIG. 4A is a schematic diagram in which multiple targets are recognizedby a neural network of deep machine learning consistent with embodimentsof the present disclosure;

FIG. 4B is a schematic diagram in which multiple targets are recognizedby a neural network of deep machine learning for improving frame rateconsistent with embodiments of the present disclosure;

FIG. 5 is a schematic diagram of an exemplary target tracking techniqueconsistent with embodiments of the present disclosure;

FIG. 6A is a schematic diagram showing an exemplary image that may beformed using a target consistent with embodiments the presentdisclosure;

FIG. 6B is a schematic diagram showing an exemplary image of the targetof FIG. 6A after movement of the target consistent with embodiments ofthe present disclosure;

FIG. 7A is a schematic diagram showing an exemplary direction that maybe determined for a target consistent with embodiments of the presentdisclosure;

FIG. 7B is a schematic diagram showing an exemplary measurement of adistance to the target of FIG. 7A consistent with embodiments of thepresent disclosure; and

FIG. 8 is a flow chart of an exemplary method that may be performed forrecognizing, tracking and autofocusing on a moving target consistentwith embodiments of the present disclosure.

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar parts.While several illustrative embodiments are described herein,modifications, adaptations and other implementations are possible. Forexample, substitutions, additions or modifications may be made to thecomponents illustrated in the drawings, and the illustrative methodsdescribed herein may be modified by substituting, reordering, removing,or adding steps to the disclosed methods. Accordingly, the followingdetailed description is not limited to the disclosed embodiments andexamples. Instead, the proper scope is defined by the appended claims.

There are previous approaches for identifying, tracking, and focusing onmoving objects/targets. For example, a camshift algorithm realizesportrait recognition and tracking using infrared imaging. It selects afocus window based on template matching and can solve problemsassociated with auto-focusing when using infrared to image movingobjects. While the camshift algorithm is suitable for tracking targetsin simple cases, it fails to track objects in more complex situations.Automatic focusing (“auto-focus”) and tracking of objects can berealized by detecting natural features of the objects such as corners,lines, or edges. Further, feature point matching based on theKanade-Lucas-Tomasi algorithm may be used to estimate camera motionvectors, and a spatial location invariant criterion may be used to checkmatching points and delete those points which were error-matchedeffectively. Although these previous approaches can effectively trackmoving objects, in complex scenes, tracking accuracy is not high, andthe calculation process is more complex. Further, in an auto-focusprocess, the movement of a target may change the scene within a focuswindow, such that a portion of the background may change in the focuswindow, causing the focus to fail or become unstable.

The disclosed embodiments of the present disclosure provide methods andsystems for multi-target tracking and focusing based on deep machinelearning and laser radar. The disclosed methods and systems may be basedon digital image processing for tracking and focusing technology, andmay be applied to various types of images and imaging systems, such ascamera, video recording, etc. Digital image processing based on deepmachine learning can effectively recognize multiple targets andaccurately track the targets. By further combining accurate distancemeasurement obtained by laser radar for only the recognized targetsrather than the entire scene, costs associated with devices can bereduced and multi-target tracking and focusing can be achieved.Accordingly, conventional problems associated with tracking and focusingon a moving target, such as a low target recognition rate, trackinginstability, and focus instability or focus failure, may be solved. Asused in the disclosed embodiments, a “target” is an object beingtracked, and a “moving target” is an object being tracked that is movingrelative to an imaging system used for tracking, such that at least oneof the imaging system and target object is moving relative to the other.

Generally, laser radar distance measurement refers to a process formeasuring a distance to a target by illuminating that target with apulsed laser light, and measuring the reflected pulses with a sensor,such as using a light detection and ranging (LiDAR) technique. Forexample, an infrared laser device may send a laser pulse in a narrowbeam towards an object, and a period of time taken by the pulse to bereflected off the object and returned to the sender of the laser pulseis determined. A distance from the object to the laser device can becalculated based on the measured time elapsed between when the pulse wastransmitted and when its reflected pulse was received and the speed oflight. Although the disclosed embodiments of the present disclosure aredescribed using such a laser radar distance measurement, other suitabledistance measurement techniques, such as ultrasonic distance measurementmay also be employed.

Deep machine learning may refer to a class of machine learningalgorithms that may use interconnected “layers” of linear and/ornonlinear processing devices or software, e.g., configured to performimage feature extraction and/or transformation, in which each successivelayer uses an output from a previous layer as its input. Deep machinelearning may be supervised (e.g., classification) or unsupervised (e.g.,pattern analysis), and higher level features may be derived from lowerlevel features to form a hierarchical representation of data (e.g.,pixels of an image). An observation (e.g., an image, audio data, etc.)that is processed by deep machine learning may be represented in manyways; for example, a vector of intensity values per pixel, a set ofedges, regions of a particular shape, sampled signals, spectralfrequencies, etc. Deep machine learning architectures may include deepneural networks, convolutional deep neural networks, deep beliefnetworks, recurrent neural networks, and so forth.

A neural network is a computational model or system based on acollection of individual neural units (i.e., neurons). The collection ofneural units may be organized into different layers of neural units, forexample, an input layer, one or more hidden layers, and an output layer.Each neural unit may be connected with many other neural units ofdifferent layers, and be computed using an activation function (e.g., asummation function). Observations (e.g., image data or patterns) may bepresented to the neural network via the input layer, which communicatesinformation corresponding to the observation to the one or more hiddenlayers where the actual processing may be done using an activationfunction and a model of weighted connections. The hidden layers may linkto the output layer, which in turn may provide an output determined fromprocessing the observation. In some embodiments, a neural network may beself-learning and trained by examples. For example, learning rules maybe applied to neural networks to modify one or more weights (e.g.,scalar parameters) of the connections according to the inputobservation.

In the disclosed embodiments, a suitable neural network is selected, andthe characteristics of corresponding layers in the network areunderstood. The neural network is preferably trained, e.g., using alarge number of samples collected in different environments. The weightsobtained from training may be used to recognize targets in the imagedata that is input to the neural network. Further, by testing the neuralnetwork using image of objects in different environments and the weightparameters determined from training the network, a tracking algorithmmay be used to track one or more target objects in the image data. Insome embodiments, one or more distance measurements by laser radar maybe used to measure a distance from an imaging device to only certaintarget objects that are of interest to a user. Automatic focusing by theimaging device on a desired target object may be achieved byautomatically adjusting focus length of the imaging device based on themeasured distance.

By focusing only on a target object of interest, for example selecting afixed focus window containing the desired target object, the time andcomputation cost associated with tracking and focusing on the targetobject can be significantly reduced, and interference resulting frombackground information can be significantly decreased, enablingreal-time target tracking and flexible selection of focus on only one ormore desired target objects.

Although the following exemplary embodiments are described in thecontext of a movable object, such as a UAV, those skilled in the artwill appreciate other implementations are possible and alternativeembodiments may be deployed without using a UAV. For example, the systemand method disclosed herein may be implemented using various imagingsystems, for example on moving or stationary objects, or as part of alarger system consistent with the disclosed embodiments.

FIG. 1 shows an exemplary movable object 10 that may be configured tomove or travel within an environment. Movable object 10 may be anysuitable object, device, mechanism, system, or machine configured totravel on or within a suitable medium (e.g., a surface, air, water,rails, space, underground, etc.). For example, movable object 10 may bea UAV. Although movable object 10 is shown and described herein as a UAVfor exemplary purposes of this description, it is understood that othertypes of movable object (e.g., wheeled objects, nautical objects,locomotive objects, other aerial objects, etc.) may also oralternatively be used in embodiments consistent with this disclosure. Asused herein, the term UAV may refer to an aerial device configured to beoperated and/or controlled automatically (e.g., via an electroniccontrol system) and/or manually by off-board personnel.

Movable object 10 may include one or more propulsion devices 12 and maybe configured to carry a payload 14. In some embodiments, as shown inFIG. 1, payload 14 may be connected or attached to movable object 10 bya carrier 16, which may allow for one or more degrees of relativemovement between payload 14 and movable object 10. In other embodiments,payload 14 may be mounted directly to movable object 10 without carrier16. Movable object 10 may also include a sensing system 18, acommunication system 20, and a controller 22 in communication with theother components.

Movable object 10 may include one or more (e.g., 1, 2, 3, 3, 4, 5, 10,15, 20, etc.) propulsion devices 12 positioned at various locations (forexample, top, sides, front, rear, and/or bottom of movable object 10)for propelling and steering movable object 10. Propulsion devices 12 maybe devices or systems operable to generate forces for sustainingcontrolled flight. Propulsion devices 12 may share or may eachseparately include or be operatively connected to a power source, suchas a motor (e.g., an electric motor, hydraulic motor, pneumatic motor,etc.), an engine (e.g., an internal combustion engine, a turbine engine,etc.), a battery bank, etc., or combinations thereof. Each propulsiondevice 12 may also include one or more rotary components 24 drivablyconnected to the power source and configured to participate in thegeneration of forces for sustaining controlled flight. For instance,rotary components 24 may include rotors, propellers, blades, nozzles,etc., which may be driven on or by a shaft, axle, wheel, hydraulicsystem, pneumatic system, or other component or system configured totransfer power from the power source. Propulsion devices 12 and/orrotary components 24 may be adjustable (e.g., tiltable) with respect toeach other and/or with respect to movable object 10. Alternatively,propulsion devices 12 and rotary components 24 may have a fixedorientation with respect to each other and/or movable object 10. In someembodiments, each propulsion device 12 may be of the same type. In otherembodiments, propulsion devices 12 may be of multiple different types.In some embodiments, all propulsion devices 12 may be controlled inconcert (e.g., all at the same speed and/or angle). In otherembodiments, one or more propulsion devices may be independentlycontrolled with respect to, e.g., speed and/or angle.

Propulsion devices 12 may be configured to propel movable object 10 inone or more vertical and horizontal directions and to allow movableobject 10 to rotate about one or more axes. That is, propulsion devices12 may be configured to provide lift and/or thrust for creating andmaintaining translational and rotational movements of movable object 10.For instance, propulsion devices 12 may be configured to enable movableobject 10 to achieve and maintain desired altitudes, provide thrust formovement in all directions, and provide for steering of movable object10. In some embodiments, propulsion devices 12 may enable movable object10 to perform vertical takeoffs and landings (i.e., takeoff and landingwithout horizontal thrust). In other embodiments, movable object 10 mayrequire constant minimum horizontal thrust to achieve and sustainflight. Propulsion devices 12 may be configured to enable movement ofmovable object 10 along and/or about multiple axes.

Payload 14 may include one or more sensory devices 19. Sensory devices19 may include devices for collecting or generating data or information,such as surveying, tracking, and capturing images or video of targets(e.g., objects, landscapes, subjects of photo or video shoots, etc.).Sensory devices 19 may include imaging devices configured to gather datathat may be used to generate images. For example, imaging devices mayinclude photographic cameras, video cameras, infrared imaging devices,ultraviolet imaging devices, x-ray devices, ultrasonic imaging devices,radar devices, etc. Sensory devices 19 may also or alternatively includedevices for capturing audio data, such as microphones or ultrasounddetectors. Sensory devices 19 may also or alternatively include othersuitable sensors for capturing visual, audio, and/or electromagneticsignals. The imaging devices may be capable of performing auto focus ona target by adjusting focus length to image the target with a desirableimage quality. The sensory devices 19 may include one or more distancemeasurement devices that measure distances from the imaging devices totargets. The distance measurement devices may implement a laser radardevice, an ultrasonic device, and/or a combination thereof.

Carrier 16 may include one or more devices configured to hold thepayload 14 and/or allow the payload 14 to be adjusted (e.g., rotated)with respect to movable object 10. For example, carrier 16 may be agimbal. Carrier 16 may be configured to allow payload 14 to be rotatedabout one or more axes, as described below. In some embodiments, carrier16 may be configured to allow 360° of rotation about each axis to allowfor greater control of the perspective of the payload 14. In otherembodiments, carrier 16 may limit the range of rotation of payload 14 toless than 360° (e.g., ≤270°, ≤210°, ≤180, ≤120°, ≤90°, ≤45°, ≤30°, ≤15°,etc.), about one or more of its axes.

Carrier 16 may include a frame assembly 26, one or more actuator members28, and one or more carrier sensors 30. Frame assembly 26 may beconfigured to couple the payload 14 to the movable object 10 and, insome embodiments, allow payload 14 to move with respect to movableobject 10. In some embodiments, frame assembly 26 may include one ormore sub-frames or components movable with respect to each other.Actuation members 28 may be configured to drive components of frameassembly relative to each other to provide translational and/orrotational motion of payload 14 with respect to movable object 10. Inother embodiments, actuator members 28 may be configured to directly acton payload 14 to cause motion of payload 14 with respect to frameassembly 26 and movable object 10. Actuator members 28 may be or includesuitable actuators and/or force transmission components. For example,actuator members 28 may include electric motors configured to providelinear or rotation motion to components of frame assembly 26 and/orpayload 14 in conjunction with axles, shafts, rails, belts, chains,gears, and/or other components.

Carrier sensors 30 may include devices configured to measure, sense,detect, or determine state information of carrier 16 and/or payload 14.State information may include positional information (e.g., relativelocation, orientation, attitude, linear displacement, angulardisplacement, etc.), velocity information (e.g., linear velocity,angular velocity, etc.), acceleration information (e.g., linearacceleration, angular acceleration, etc.), and or other informationrelating to movement control of carrier 16 or payload 14 with respect tomovable object 10. Carrier sensors 30 may include one or more types ofsuitable sensors, such as potentiometers, optical sensors, visionssensors, magnetic sensors, motion or rotation sensors (e.g., gyroscopes,accelerometers, inertial sensors, etc.). Carrier sensors 30 may beassociated with or attached to various components of carrier 16, such ascomponents of frame assembly 26 or actuator members 28, or movableobject 10. Carrier sensors 30 may be configured to communicate data andinformation with controller 22 via a wired or wireless connection (e.g.,RFID, Bluetooth, Wi-Fi, radio, cellular, etc.). Data and informationgenerated by carrier sensors 30 and communicated to controller 22 may beused by controller 22 for further processing, such as for determiningstate information of movable object 10 and/or targets.

Carrier 16 may be coupled to movable object 10 via one or more dampingelements configured to reduce or eliminate undesired shock or otherforce transmissions to payload 14 from movable object 10. Dampingelements may be active, passive, or hybrid (i.e., having active andpassive characteristics). Damping elements may be formed of any suitablematerial or combinations of materials, including solids, liquids, andgases. Compressible or deformable materials, such as rubber, springs,gels, foams, and/or other materials may be used as damping elements. Thedamping elements may function to isolate payload 14 from movable object10 and/or dissipate force propagations from movable object 10 to payload14. Damping elements may also include mechanisms or devices configuredto provide damping effects, such as pistons, springs, hydraulics,pneumatics, dashpots, shock absorbers, and/or other devices orcombinations thereof.

Sensing system 18 may include one or more sensors associated with one ormore components or other systems of movable device 10. For instance,sensing system may include sensors for determining positionalinformation, velocity information, and acceleration information relatingto movable object 10 and/or targets. In some embodiments, sensing systemmay also include carrier sensors 30. Components of sensing system 18 maybe configured to generate data and information that may be used (e.g.,processed by controller 22 or another device) to determine additionalinformation about movable object 10, its components, or its targets.Sensing system 18 may include one or more sensors for sensing one ormore aspects of movement of movable object 10. For example, sensingsystem 18 may include sensory devices associated with payload 14 asdiscussed above and/or additional sensory devices, such as a positioningsensor for a positioning system (e.g., GPS, GLONASS, Galileo, Beidou,GAGAN, etc.), motion sensors, inertial sensors (e.g., IMU sensors),proximity sensors, image sensors, etc. Sensing system 18 may alsoinclude sensors or be configured to provide data or information relatingto the surrounding environment, such as weather information (e.g.,temperature, pressure, humidity, etc.), lighting conditions (e.g.,light-source frequencies), air constituents, or nearby obstacles (e.g.,objects, structures, people, other vehicles, etc.).

Sensing system 18 may include one or more light emitters and sensors forperforming a laser radar distance measurement, such as for making aLiDAR measurement to determine a distance from the movable object to atarget object. In some embodiments, a LiDAR laser and correspondingsensor may be mounted anywhere on the movable object 10, or may beattached to the movable object as a separate module, such as on carrier16, or included in any other device or sensor on the movable object.

Communication system 20 may be configured to enable communications ofdata, information, commands, and/or other types of signals betweencontroller 22 and off-board entities. Communication system 20 mayinclude one or more components configured to send and/or receivesignals, such as receivers, transmitter, or transceivers that areconfigured to carry out one- or two-way communication. Components ofcommunication system 20 may be configured to communicate with off-boardentities via one or more communication networks, such as radio,cellular, Bluetooth, Wi-Fi, RFID, and/or other types of communicationnetworks usable to transmit signals indicative of data, information,commands, and/or other signals. For example, communication system 20 maybe configured to enable communications between devices for providinginput for controlling movable object 10 during flight, such as a controlterminal (“terminal”) 32.

Terminal 32 may be configured to receive input, such as input from auser (i.e., user input), and communicate signals indicative of the inputto controller 22. Terminal 32 may be configured to receive input andgenerate corresponding signals indicative of one or more types ofinformation, such as control data (e.g., signals) for moving ormanipulating movable device 10 (e.g., via propulsion devices 12),payload 14, and/or carrier 16. Terminal 32 may also be configured toreceive data and information from movable object 10, such as operationaldata relating to, for example, positional data, velocity data,acceleration data, sensory data, and other data and information relatingto movable object 10, its components, and/or its surroundingenvironment. Terminal 32 may be a remote control with physical sticksconfigured to control flight parameters, or may be a touch screendevice, such as a smartphone or a tablet, with virtual controls for thesame purposes, and may employ an application on a smartphone or atablet, or a combination thereof.

In some embodiments, terminal 32 may be a smart eyeglass. As usedherein, the smart eyeglass may include any wearable computer glasses orother wearable item that can provide additional information to an imageor scene that a wearer sees. The smart eyeglass may include an opticalhead-mounted display (OHMD) or embedded wireless glasses withtransparent heads-up display (HUD) or augmented reality (AR) overlaythat has the capability of reflecting projected digital images as wellas allowing the user to see through it, or see better with it. The smarteyeglass may serve as a front end display for images, videos, and otherdata or information received from the movable object 10, for example,via cellular technology or Wi-Fi. In some embodiments, the smarteyeglass may also control the movable object 10 via natural languagevoice commands and/or use of touch buttons on the smart eyeglass.

In the example shown in FIGS. 2A and 2B, terminal 32 may includecommunication devices 34 that facilitate communication of informationbetween terminal 32 and other entities, such as movable object 10 oranother terminal 32. Communication devices 34 may include antennae orother devices configured to send or receive signals. Terminal 32 mayalso include one or more input devices 36 configured to receive inputfrom a user for communication to movable object 10. FIG. 2A shows oneexemplary embodiment of terminal 32 having an input device 36 with aplurality of input devices 38, 40, 42, and 44 configured to receive userinputs indicative of desired movements of movable object 10 or itscomponents. It is understood, however, that other possible embodimentsor layouts of terminal may be possible and are within the scope of thisdisclosure.

Terminal 32 may include input devices, such as input levers 38 and 40,buttons 42, triggers 44, and/or other types of input devices forreceiving one or more inputs from the user. Each input device ofterminal 32 may be configured to generate an input signal communicableto controller 22 and usable by controller 22 as inputs for processing.In addition to flight control inputs, terminal 32 may be used to receiveuser inputs of other information, such as manual control settings,automated control settings, control assistance settings etc., which maybe received, for example, via buttons 42 and/or triggers 44. It isunderstood that terminal 32 may include other or additional inputdevices, such as buttons, switches, dials, levers, triggers, touch pads,touch screens, soft keys, a mouse, a keyboard, a voice recognitiondevice, and/or other types of input devices.

As shown in FIG. 2B, terminal 32 may also include a display device 46configured to display and/or receive information to and/or from a user.For example, terminal 32 may be configured to receive signals frommovable object 10, which signals may be indicative of information ordata relating to movements of movable object 10 and/or data (e.g.,imaging data) captured using movable object 10 (e.g., in conjunctionwith payload 14). In some embodiments, display device 46 may be amultifunctional display device configured to display information on amultifunctional screen 48 as well as receive user input via themultifunctional screen 48. For example, in one embodiment, displaydevice 46 may be configured to receive one or more user inputs viamultifunctional screen 48. In another embodiment, multifunctional screen48 may constitute a sole input device for receiving user input.

In some embodiments, terminal 32 may be or include an interactivegraphical interface for receiving one or more user inputs. That is,terminal 32 may be a graphical user interface (GUI) and/or include oneor more graphical versions of input devices 36 for receiving user input.Graphical versions of terminal 32 and/or input devices 36 may bedisplayable on a display device (e.g., display device 46) or amultifunctional screen (e.g., multifunctional screen 48) and includegraphical features, such as interactive graphical features (e.g.,graphical buttons, text boxes, dropdown menus, interactive images,etc.). For example, in one embodiment, terminal 32 may include graphicalrepresentations of input levers 38 and 40, buttons 42, and triggers 44,which may be displayed on and configured to receive user input viamultifunctional screen 48. In some embodiments, terminal 32 may beconfigured to receive all user inputs via graphical input devices, suchas graphical versions of input devices 36. Terminal 32 may be configuredto generate graphical versions of input devices 36 in conjunction with acomputer application (e.g., an “app”) to provide an interactiveinterface on the display device or multifunctional screen of anysuitable electronic device (e.g., a cellular phone, a tablet, etc.) forreceiving user inputs.

In some embodiments, display device 46 may be an integral component ofterminal 32. That is, display device 46 may be attached or fixed toterminal 32. In other embodiments, display device may be connectable to(and dis-connectable from) terminal 32. That is, terminal 32 may beconfigured to be electronically connectable to display device 46 (e.g.,via a connection port or a wireless communication link) and/or otherwiseconnectable to terminal 32 via a mounting device 50, such as by aclamping, clipping, clasping, hooking, adhering, or other type ofmounting device.

In some embodiments, terminal 32 may be configured to communicate withelectronic devices configurable for controlling movement and/or otheroperational aspects of movable object 10. For example, display device 46may be a display component of an electronic device, such as a cellularphone, a tablet, a personal digital assistant, a laptop computer, orother device. In this way, users may be able to incorporate thefunctionality of other electronic devices into aspects of controllingmovable object 10, which may allow for more flexible and adaptablecontrol schemes to be used. For example, terminal 32 may be configuredto communicate with electronic devices having a memory and at least oneprocessor, which control devices may then be used to provide user inputvia input devices associated with the electronic device (e.g., amultifunctional display, buttons, stored apps, web-based applications,etc.). Communication between terminal 32 and electronic devices may alsobe configured to allow for software update packages and/or otherinformation to be received and then communicated to controller 22 (e.g.,via communication system 20).

It is noted that other control conventions that relate inputs receivedvia terminal 32 to desired or actual movements of movable device 10 maybe used, if desired.

As shown in FIG. 3, controller 22 may include one or more components,for example, a memory 52 and at least one processor 54. Memory 52 may beor include at least one non-transitory computer readable medium and caninclude one or more memory units of non-transitory computer-readablemedium. Non-transitory computer-readable medium of memory 52 may be orinclude any type of volatile or non-volatile memory device, for exampleincluding floppy disks, optical discs, DVD, CD-ROMs, microdrive, andmagneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flashmemory devices, magnetic or optical cards, nanosystems (includingmolecular memory ICs), or any type of media or device suitable forstoring instructions and/or data. Memory units may include permanentand/or removable portions of non-transitory computer-readable medium(e.g., removable media or external storage, such as an SD card, RAM,etc.).

Information and data from sensing system 18 may be communicated to andstored in non-transitory computer-readable medium of memory 52. Thecomputer-readable medium associated with memory 52 may also beconfigured to store logic, code and/or program instructions executableby processor 54 to perform any suitable embodiment of the methodsdescribed herein. For example, the computer-readable medium associatedwith memory 52 may be configured to store computer-readable instructionsthat, when executed by processor 54, cause the processor to perform amethod comprising one or more steps. The method performed by theprocessor based on the instructions stored in the non-transitorycomputer readable medium may involve processing inputs, such as inputsof data or information stored in the non-transitory computer-readablemedium of memory 52, inputs received from terminal 32, inputs receivedfrom sensing system 18 (e.g., received directly from sensing system orretrieved from memory), and/or other inputs received via communicationsystem 20. The non-transitory computer-readable medium may be configuredto store sensing data from the sensing module to be processed by theprocessing unit. In some embodiments, the non-transitorycomputer-readable medium can be used to store the processing resultsproduced by the processing unit.

The sensory device 19 in FIG. 1 may be embodied by the imaging system 19in the exemplary embodiment of FIG. 3. In this disclosed embodiment,imaging system 19 may include imaging devices configured to gather datathat may be used to generate images for surveying, tracking, andcapturing images or video of targets (e.g., objects, landscapes,subjects of photo or video shoots, etc.). For example, imaging devicesmay include photographic cameras, video cameras, infrared imagingdevices, ultraviolet imaging devices, x-ray devices, ultrasonic imagingdevices, radar devices, etc. In this exemplary embodiment, the imagingdevice may be configured to generate optical data of the target foridentifying and tracking the target. For example, the imaging device maybe an optical device, such as a camera or video camera. The imagingdevice may be configured to generate imaging data indicative of one ormore features of the target. The imaging system 19 may further beconfigured to communicate data (e.g., image frames) and information withcontroller 22 via a wired or wireless connection (e.g., RFID, Bluetooth,Wi-Fi, radio, cellular, etc.). Data and information generated by imagingsystem 19 and communicated to controller 22 may be used by controller 22for further processing.

Processor 54 may include one or more processors and may embody aprogrammable processor, e.g., a central processing unit (CPU). Processor54 may be operatively coupled to memory 52 or another memory deviceconfigured to store programs or instructions executable by processor 54for performing one or more method steps. It is noted that method stepsdescribed herein may be stored in memory 52 and configured to be carriedout by processor 54 to cause the method steps to be carried out by theprocessor 54.

In some embodiments, processor 54 may include and/or alternatively beoperatively coupled to one or more control modules, such as a targetrecognition module 56, a target tracking module 58, a target distancemodule 60, and a target focusing module 62, which will be explained ingreater detail below. The target recognition module 56, the targettracking module 58, the target distance module 60, and the targetfocusing module 62 may be implemented in software for execution onprocessor 54, or may be implemented in hardware and/or softwarecomponents separate from processor 54 (not shown in the figure).

The target recognition module 56 may be configured to recognize one ormore targets that appear in images or videos captured by the imagingsystem 19. The target recognition module may be implemented by anysuitable techniques that include, but are not limited to, deep machinelearning. For example, in the example of deep machine learning, completeimages or video frames as the input are received by a neural network,and the neural network may divided the complete image or video frameinto different regions. The neural network may further predict boundingboxes of each region, and the probability of a target appearing in aregion. A deep machine learning algorithm used herein may be anysuitable deep machine learning algorithm may include commerciallyavailable deep machine learning software packages, for example, YOLO(You Only Look Once) real-time target detection system.

For example, the neural network of YOLO (version 2) includes 32 layersin which 22 layers are convolution layers, which can efficientlyidentify targets and detect the target region containing a targetobject. This neural network may be used to accurately track the targetin accordance with the disclosed embodiments.

In some embodiments, off-line training a neural network of deep machinelearning may be performed. For example, a variety of training samplesmay be prepared and labeled. The training samples may include a largenumber of samples captured in different environments, circumstances, andscenes. The labeled training samples may be input to a neural networkfor off-line training to learn a large number of object features, suchthat multiple targets can be accurately recognized in a real-time mannerby the UAV. The weights after training may be tested. After multipleiterations, an operative set of weight parameters may be obtained basedon the input training samples. FIG. 4A shows a schematic diagram inwhich multiple objects (e.g., dog, car, bike) and their correspondingregions (bounding boxes) in an image may be identified using a neuralnetwork. One or more of these identified objects then may be designatedas target objects for tracking.

In some embodiments, a different neural network may be employed toimprove the image frame rate processing. For example, Tiny YOLO neuralnetworks may be utilized, which include 17 layers, including 8convolution layers. FIG. 4B shows a schematic diagram in which multipletarget objects (e.g., cow, person, sheep) and their correspondingregions (bounding boxes) in an image may be identified using a neuralnetwork configured to improve image frame rate processing.

The target tracking module 58 may be configured to track one or moretargets that are already accurately recognized by the target recognitionmodule 56. Once the one or more targets are recognized, the targettracking module 58 may use a target tracking algorithm to track thetargets. Such tracking algorithm may help control propulsion devices 12of movable object 10 to adjust the spatial disposition, velocity, and/oracceleration of the movable object 10 with respect to six degrees offreedom (e.g., three translational directions along its coordinate axesand three rotational directions about its coordinate axes) to enablemovable object 10 to automatically track a desired target object.

With reference to FIG. 5, target tracking may be performed inconjunction with a sensory device (i.e., sensory device 19 in FIG. 1 andimaging system 19 in FIG. 3), such as a camera 64, connected to movableobject 10. Camera 64 may be configured to capture an image containing atarget object (e.g. the dog in FIG. 4A or the sheep in FIG. 4B), on animage plane 66 in an image coordinate system 67. A target image 65 ofthe target object may be formed in the image plane 66, and a boundingbox 68 may be generated around the target image 65.

Target image 65 may be represented based on an aperture imaging model,which assumes that a light ray from an object point in a threedimensional space can be projected onto a two-dimensional image plane toform an image point. Camera 64 may include an optical axis 69, e.g.,measured from a center 70 of the camera, and a focal length 72. Whenoptical axis 69 passes through both the camera center 70 and the centerof image plane 66, the distance between the camera center 70 and thecenter of image plane 66 can be equal or substantially the same distanceas the camera's focal length 72.

The projected relative distance 74 on the ground between camera center70 and the target 80 (e.g., the distance from camera 64 and the targetobject) can then be determined based on geometric relationships andcoordinate transformations. For example, the target 80 may have a toptarget point (x_(t), y_(t), z_(t)) and a bottom target point (x_(b),y_(b), z_(b)) in a world coordinate system, which may be projected onimage plane 66 as a top image point (u_(t), v_(t)) and a bottom imagepoint (u_(b), v_(b)) respectively. A top line passes through cameracenter 70 and the top image point at a first tilt angle 76 with respectto the Z axis of the world coordinate system. Also, a bottom line passesthrough the camera center 70 and the bottom image point at a second tiltangle 78 from the Z axis.

Direction vectors {right arrow over (T)} and {right arrow over (B)}representing the top and bottom lines from camera 64 to the top andbottom of the target image 65 can be expressed as the following

$\overset{\rightarrow}{T} = {\left. \begin{pmatrix}x_{t} \\y_{t} \\z_{t}\end{pmatrix} \right.\sim{{RK}^{- 1}\begin{pmatrix}u_{t} \\v_{t} \\1\end{pmatrix}}}$ $\overset{\rightarrow}{B} = {\left. \begin{pmatrix}x_{b} \\y_{b} \\z_{b}\end{pmatrix} \right.\sim{{RK}^{- 1}\begin{pmatrix}u_{b} \\v_{b} \\1\end{pmatrix}}}$

where K represents the intrinsic matrix of the camera, and R representsthe camera rotation.

The distance 74 to the target can then be determined based on the heightof the camera h_(c) and position of the bounding box 68 in the imagecoordinate system 67. For example, the distance 74 to the target object80 can be calculated as d=−h_(c)/z_(b)*P_(b), and the target height canbe calculated as h_(o)=h_(c)+z_(t)d/P_(t), where h_(c) is the measuredor effective height of the camera, P_(b) is the projection length of{right arrow over (B)} on the ground, and P_(t) is the projection lengthof {right arrow over (T)} on the ground, which are defined as in thefollowing.

P _(b)=√{square root over (x _(b) ² +y _(b) ²)}

P _(t)=√{square root over (x _(t) ² +y _(t) ²)}

Thereafter, the system can estimate the linear distance 74 (e.g., alongthe X-axis) to the target, even when the target altitude changes (e.g.when the target traverses rough terrain, inclines, stairs, climbsobjects, hovers or flies at varying altitudes, etc.). The projectedrelative distance 74 on the ground between the target object 80 and themovable object 10 can be calculated as h_(c)/dh, where dh present theestimate height of the target at a unit distance away from the camera,which can be computed using the following formula.

${dh} = {{\frac{\overset{\rightarrow}{T}}{P_{t}} - \frac{\overset{\rightarrow}{B}}{P_{b}}}}$

In accordance with some embodiments, to provide target tracking,controller 22 may be configured to automatically, by the target trackingmodule 60, control propulsion device 12 in order to maintain a constantdistance 74 at desired or default values during flight. For example, thetarget tracking module 60 may be configured to continually orperiodically calculate the distance 74 and use feedback control (e.g.,PID control) to maintain the distance 74 at a desired value (e.g., inputby the user via terminal 32) or a default value. Target tracking may beconducted even when the height of movable object 10 changes, such aswhen movable object traverses rough terrain, slopes, other objects,etc., while tracking a target.

In some embodiments, tracking can be performed even if the imagingsystem 19 is in the process of capturing one or more images. The one ormore images may have a focus on a first target in the images, whiletracking can be performed on several other targets, e.g. each surroundedwith a respective bounding box. In such embodiments, when focus isswitched from being on the first target to a second target that is oneof the several other targets (e.g., another target in the one or moreimages), the system can perform a refocusing operation based on adistance measured to the second target. Further to these disclosedembodiments, measuring the distance to the second target can bedetermined by scanning a region in the bounding box surrounding thesecond target instead of scanning the whole region of the image. Thus,the system in these embodiments may have a further advantage in terms ofreducing processing time and cost.

Also, in some embodiments, tracking may be performed only for a targetof interest, e.g., to reduce costs and increase the frame rate. In sucha scenario, the target of interest may be identified via a neuralnetwork of deep machine learning which recognizes the target of interestto be within a certain region of an image, for example, recognizing thedog within a bounding box in FIG. 4A. In such embodiments, the systemmay reduce computational costs for tracking, focusing on, and imagingthe target of interest. Further to these embodiments, when the target ofinterest is switched, for example, from a first target (e.g., the dog inFIG. 4A) to a second target (e.g., the car in FIG. 4A), tracking of thesecond target (e.g., the car) can be performed fast and efficiently dueto the close proximity of the second target to the first target, therebynot only reducing computational costs associated with tracking, focusingon, and imaging the second target, but also increasing the frame rate ofimaging targets.

In some embodiments, tracking may involve the movement of the UAV or maybe performed from a stationary object.

The target distance module 60 may be configured to determine a distanceto a target object from the imaging system 19, for example. After atarget is identified, recognized, and tracked, accurate focus isemployed to acquire a high quality image of the target. Due to therelative movement between the target object and the movable object 10,the distance to the target object from the imaging system 19 may changeconstantly. To get an accurate focus on the target, the distance to thetarget needs to be measured in a real-time manner.

FIG. 6A shows a schematic diagram of an exemplary embodiment for formingan image of a target object prior to moving relative to the targetobject. As shown in FIG. 6A, an image S₀′ of the target S is formed onan imaging plane. The distance from the target object and the image tothe image plane are −l and l′ respectively. The height of the image S₀′is −h₀′ as defined in equation (1):

$\begin{matrix}{{- h_{0}^{\prime}} = {\frac{l^{\prime}}{- l} \times h}} & (1)\end{matrix}$

where

$\frac{l^{\prime}}{- l}$

is an image magnification. When the target object moves, as shown inFIG. 6B, the object distance and the image distance are changed to−l+Δl′ and L′−Δl′ respectively. The height of an image S₁′ of the targetS is changed to −h₁′ as defined in equation (2):

$\begin{matrix}{{- h_{1}^{\prime}} = {\frac{l^{\prime} - {\Delta \; l^{\prime}}}{{- l} + {\Delta \; l^{\prime}}} \times h}} & (2)\end{matrix}$

where

$\frac{l^{\prime} - {\Delta \; l^{\prime}}}{{- l} + {\Delta \; l^{\prime}}}$

is an image magnification after moving of the target. It can be seen dueto the movement of the target, the target image has changed.

By combining equations (1) and (2), equation (3) can be determined as:

$\begin{matrix}{{- h_{1}^{\prime}} = {\frac{l^{\prime} - {\Delta \; l^{\prime}}}{{- l} + {\Delta \; l^{\prime}}} \times \frac{- l}{l^{\prime}} \times \left( {- h_{0}^{\prime}} \right)}} & (3)\end{matrix}$

Thus, an offset of image height can be expressed in equation (4) as:

$\begin{matrix}{{{{- h_{0}^{\prime}} - \left( {- h_{1}^{\prime}} \right)}} = {{{1 - {\frac{l^{\prime} - {\Delta \; l^{\prime}}}{l^{\prime}} \times \frac{- l}{{- l} + {\Delta \; l^{\prime}}}}}} \times \left( {- h_{0}^{\prime}} \right)}} & (4)\end{matrix}$

From the equation (4), it can be see that, for targets having the sameinitial image heights and angles of view, the greater the target objectdistance, the smaller the image height offset. However, even when atarget object distance is infinity, an image height offset is stillpresent as shown in equation (5):

$\begin{matrix}{{\lim_{{- l}\rightarrow\infty}{{{- h_{0}^{\prime}} - \left( {- h_{1}^{\prime}} \right)}}} = {{\frac{\Delta \; l^{\prime}}{l^{\prime}}} \times {\left( h_{0}^{\prime} \right).}}} & (5)\end{matrix}$

Accordingly, when the target moves, the target object distance must beaccurately measured, and accurate focus may be achieved by driving afocus control mechanism in the imaging system 19 based on the measuredobject distance.

In some embodiments, to reduce costs and increase the frame rate, thedistance measurement may be performed only for a target object ofinterest. For example, a neural network of deep machine learningrecognizes the target of interest and a region containing the target ofinterest (i.e., an bounding box). According to the bounding box, acenter position of the target object and a direction of the centerposition may be calculated, as shown FIG. 7A. In FIG. 7A, a centerposition t of the target object is shown on an image plane having acenter point P and an image coordinate system, and a direction of thecenter position is exhibited by connecting a center C of a camera (e.g.,the imaging system 19) to the center position t of the target object. InFIG. 7B, assuming that the pixel position of the target center positiont in the image coordinate system is (u, v), then v may be expressed inequation (6) as:

$\begin{matrix}{v = \frac{fY}{Z}} & (6)\end{matrix}$

where f is the focal length of the camera that may be obtained bycalibrating the camera, and Y and Z are coordinates in the cameracoordinate system (i.e., a world coordinate system). An angle α of thedirection of the center position t of the target can be determined inequation (7) as:

$\begin{matrix}{{\tan \; \alpha} = \frac{v}{f}} & (7)\end{matrix}$

A distance measurement device, such as a laser radar as shown in FIG.7B, is used to measure distances to the target within a range of thecenter position direction. The measurement device may be in a very closeproximity of the camera, such that, the laser radar can measure adistance to the target of interest by scanning a certain range based onthe angle α of the center position direction, for example, within anangle range of ψ as shown in FIG. 7B. The target focusing module 62 maybe configured to control a focus control mechanism/module in the imagingsystem 19. The focus control mechanism may be a built-in mechanism inthe imaging system 10, or may be any focus control mechanism that isknown to those skilled in the art. After a distance to the target objectis determined, the focus control mechanism may be activated toautomatically adjust a focal length of the imaging system 19 to achievean accurate focus on the target of interest according to the measureddistance.

Processor 54 can be operatively coupled to the communication system 20and be configured to transmit and/or receive data from one or moreexternal devices (e.g., terminal 32, display device 46, or other remotecontroller). Any suitable means of communication can be used to transferdata and information to or from controller 22, such as wiredcommunication or wireless communication. For example, communicationsystem 20 can utilize one or more of local area networks (LAN), widearea networks (WAN), infrared, radio, Wi-Fi, point-to-point (P2P)networks, telecommunication networks, cloud communication, and the like.Optionally, relay stations, such as towers, satellites, or mobilestations, can be used. Wireless communications can be proximitydependent or proximity independent. In some embodiments, line-of-sightmay or may not be required for communications. The communication system20 can transmit and/or receive one or more of sensing data from thesensing system 18, processing results produced by the processor 54,predetermined control data, user commands from terminal 32 or a remotecontroller, and the like.

The components of controller 22 can be arranged in any suitableconfiguration. For example, one or more of the components of thecontroller 22 can be located on the movable object 10, carrier 16,payload 14, terminal 32, sensing system 18, or an additional externaldevice in communication with one or more of the above. In someembodiments, one or more processors or memory devices can be situated atdifferent locations, such as on the movable object 10, carrier 16,payload 14, terminal 32, sensing system 18, additional external devicein communication with one or more of the above, or suitable combinationsthereof, such that any suitable aspect of the processing and/or memoryfunctions performed by the system can occur at one or more of theaforementioned locations.

In accordance with the disclosed embodiments, FIG. 8 shows an exemplarymethod 800 that may be used for recognizing, tracking, and focusing on atarget based on deep marching learning and laser radar. Although onetarget is described here for exemplary purpose only, the method 800 maybe applied to multiple targets.

In step 802, a target object is recognized in an image. The target maybe any object identified in the image, such as a dog or a car in FIG.4A, and may move relative an imaging system. The target object may becaptured in one or more images or a video by the imaging system. Asdescribed above, the target object may be identified and recognized inthe one or more images or the video frames by a neural network andalgorithm of deep machine learning technique. The neural network andalgorithm of deep machine learning technique may be implemented in thecontroller 22 of the movable object 10.

In step 804, the target is tracked. As described above, tracking thetarget while the target is moving may be achieved by controllingpropulsion devices 12 of movable object 10 to adjust the spatialdisposition, velocity, and/or acceleration of the movable object 10 withrespect to six degrees of freedom (e.g., three translational directionsalong its coordinate axes and three rotational directions about itscoordinate axes) to enable movable object 10 to automatically track thetarget.

In step 806, a distance to the target from the imaging system 19 may bedetermined. Once a target of interest can be tracked, the distance tothe target from the imaging system 10 may be measured, for example, by alaser radar device. The laser radar device may be embedded in orattached to the imaging system 19. Alternatively, the laser radar systemmay be a stand-alone device that coordinate with the imaging system formeasuring a distance. The laser radar device may emit an infrared laserpulse, or any other laser pulse or beam at a desired frequency, towardsthe target, and receive light beams reflected off the target object. Thedistance to the target from the laser radar device/imaging system 19 maybe calculated using a total travelling time of the light beam travelingback and forth between the target and the laser radar device, and thelight speed.

In step 808, focus on the target is performed based on the determineddistance. When a distance is determined for the moving target, the focallength of the imaging system may be adjusted automatically by a focuscontrol mechanism of the imaging system 19 based on the measureddistance.

The disclosed embodiments of the present disclosure provides methods andsystems for identifying and recognizing multiple targets based on neuralnetworks of deep machine learning. Multiple targets can be recognizedeffectively in different environments, circumstances, and scenes. Thelaser radar device is simple with a high reliability and accurate focuscan be achieved automatically based on the distance determined by thelaser radar device. Images of moving target objects can be capturedusing high-quality imaging devices. Further, dark objects, or objectsbehind glass, can also be autofocused in accordance with the disclosedembodiments. Accordingly, the disclosed embodiments of the presentdisclosure employ a combination of laser ranging with neuralnetworks/deep learning for object tracking.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the disclosed methods andsystems. Other embodiments will be apparent to those skilled in the artfrom consideration of the specification and practice of the disclosedmethods and systems. It is intended that the specification and examplesbe considered as exemplary only, with a true scope being indicated bythe following claims and their equivalents

What is claimed is:
 1. A method for recognizing, tracking, and focusingon a moving target, the method comprising: recognizing the moving targettravelling relative to an imaging device; tracking the moving target;and determining a distance to the moving target from the imaging device.2. The method of claim 1, further comprising focusing on the movingtarget based on the determined distance.
 3. The method of claim 1,wherein recognizing the moving target is performed by a neural networkof deep machine learning.
 4. The method of claim 3, wherein the neuralnetwork includes one or more convolution layers.
 5. The method of claim3, wherein the neural network is trained by a number of sample images.6. The method of claim 1, wherein the imaging device is installed on amovable object.
 7. The method of claim 6, wherein tracking the movingtarget is performed by controlling propulsion devices of the movableobject.
 8. The method of claim 1, wherein determining the distance isperformed by a laser measurement device.
 9. The method of claim 1,wherein determining the distance is performed by an ultrasonicmeasurement device.
 10. The method of claim 2, wherein focusing on themoving target is performed by adjusting a focus length of the imagingdevice.
 11. The method of claim 1, wherein the moving target is one ormore of multiple moving targets.
 12. A system for recognizing, tracking,and focusing on a moving target, the system comprising: a controllerhaving one or more processors and being configured to: recognize themoving target travelling relative to an imaging device; track the movingtarget; and determine a distance to the moving target from the imagingdevice.
 13. The system of claim 12, wherein the controller is furtherconfigured to focus on the moving target based on the determineddistance.
 14. The system of claim 12, wherein recognizing the movingtarget is performed by a neural network of deep machine learning. 15.The system of claim 14, wherein the neural network includes one or moreconvolution layers.
 16. The system of claim 14, the neural network istrained by a number of sample images.
 17. The system of claim 12,wherein the imaging device is installed on a movable object.
 18. Thesystem of claim 17, wherein tracking the moving target is performed bycontrolling propulsion devices of the movable object.
 19. The system ofclaim 12, wherein determining the distance is performed by a lasermeasurement device.
 20. The system of claim 12, wherein determining thedistance is performed by an ultrasonic measurement device.
 21. Thesystem of claim 13, wherein the controller is further configured toadjust a focus length of the imaging device.
 22. The system of claim 12,wherein the moving target is one or more of multiple moving targets 23.An unmanned aerial vehicle (UAV) system, comprising: a controller incommunication with multiple devices and configured to recognize, trackand focus a moving target, the controller comprising one or moreprocessors configured to: recognize the moving target travellingrelative to an imaging device; track the moving target; and determine adistance to the moving target from the imaging device.
 24. The UAV ofclaim 23, wherein the controller is further configured to focus on themoving target based on the determined distance.
 25. The UAV of claim 23,wherein recognizing the moving target is performed by a neural networkof deep machine learning.
 26. The UAV of claim 25, wherein the neuralnetwork includes one or more convolution layers.
 27. The UAV of claim25, the neural network is trained by a number of sample images.
 28. TheUAV of claim 23, wherein determining the distance is performed by alaser measurement device.
 29. The UAV of claim 24, wherein thecontroller is further configured to adjust a focus length of the imagingdevice.
 30. A non-transitory computer-readable medium storinginstructions, that, when executed, cause a computer to perform a methodfor recognizing, tracking, and focusing on a moving target, the methodcomprising: recognizing the moving target travelling relative to animaging device; tracking the moving target; and determining a distanceto the moving target from the imaging device.